PURPOSES : This study was conducted to prevent slip accidents on manhole covers located on sidewalks and local roads as well as to propose reasonable slip resistance management standards for manhole covers. METHODS : Using field surveys, test groups were classified based on the patterns and wear amounts of the manhole covers. Standards for measuring the equipment and methods for slip resistance were established, and the slip resistance values were compared and analyzed for each manhole cover test group. RESULTS : According to the slip resistance test results, micro-protrusions on the non-slip manhole covers were found to be effective in improving slip resistance. However, in areas without microprotrusions, the improvement in slip resistance was minimal and yielded results similar to those of standard manhole covers. In addition, among the pattern types of standard manhole covers, the radial pattern was found to be the most susceptible to slipping. Under the current wear measurement standards, the change in slip resistance at different wear stages was found to be relatively small. Moreover, manhole covers had the lowest slip resistance among road surface structures, indicating the need to establish management standards for them. CONCLUSIONS : To prevent pedestrian slip accidents on sidewalks and local roads, it is necessary to ensure that the slip resistance standards of manhole covers are higher than those of sidewalks.

우리나라에서는 「모빌리티 혁신 및 활성화 지원에 관한 법률」을 제정하여 전국적으로 첨단모빌리티 사업을 활성화할 수 있는 틀 을 마련하였다. 그러나 모빌리티혁신법 내 첨단모빌리티 수단이 이용하는 친화적 도로설계에 대한 가이드라인이 부재한 상황이다. 본 연구에서는 모빌리티혁신법 내 제9조 ‘첨단모빌리티 친화적 도로환경 조성’의 원활한 사업 시행을 위해 디지털 인프라를 중심으로 가 이드라인을 제안한다. 친화적 도로를 이용하는 첨단모빌리티 도로 대상을 선정한 후 이를 토대로 요구되는 디지털 인프라를 고려하였 다. 디지털 인프라는 도로에 대한 정보를 디지털화 하는 것을 목적으로 설정하여 ① 디지털 도로, ②디지털 관리, ③디지털 트윈 3가 지로 구분지어 가이드라인을 제시하였다. 이는 지방자치단체에서 첨단모빌리티 사업 시행 시 필수적으로 고려해야 할 인프라를 검토 할 수 있을 것이다

자율주행차량을 상용화하기 위한 노력이 계속되고 있으며, 완전 자율주행 교통 환경이 조성되기 전까지 자율주행차량과 일반 차량 이 혼재된 혼합교통류가 형성될 것이라 예상된다. 이러한 혼합교통류에서 자율주행차량과 일반 차량은 주행 행태가 다르므로 기존에 는 발생하지 않았던 사고 위험상황을 유발할 수 있으며, 따라서 자율주행차량의 도입에 따른 사고 위험상황을 사전에 파악하고 이에 대한 안전관리 전략을 마련할 필요가 있다. 이러한 안전관리 전략 수립의 첫 단계로 자율주행차량 도입 시 자율주행차량이 사고위험 상황에 처할 수 있는 취약 구간과 취약 상황을 정의해야 한다. 기존 연구의 경우 자율주행 취약 구간 및 취약 상황 정의를 위해 전문 가 설문 조사 방법을 사용하였으며, 자율주행차량 데이터 구득에 어려움이 있어 주로 시뮬레이션 분석을 진행하였다. 본 연구에서는 더 실질적이고 구체적인 자율주행 취약 구간과 취약 상황을 정의하기 위해 두 가지 출처의 데이터를 활용하였으며, 다양한 방법론을 적용하여 과학적이고 다각적인 분석 결과를 도출하였다. 세종시 자율주행 실증구간에서 수집할 수 있는 자율주행차량 주행 궤적 데이 터를 활용해서는 사고위험 판단 안전 지표를 기준으로 사고 취약 구간 및 상황을 정의하였으며, 캘리포니아 DMV 자율주행차량 사고 데이터를 활용해서 연관규칙 기법과 토픽 모델링을 적용해 자율주행 사고에 영향을 미친 주요 요인들과 요인들 간의 연관성을 분석하 였다. 최종적으로는 세종시 자율주행차량 데이터 분석 결과와 캘리포니아 DMV 사고보고서 결과를 종합하여 종합적인 자율주행 취약 구간 및 상황을 정의하였다. 향후 본 연구에서 정의한 자율주행 취약 구간과 취약 상황 및 본 연구의 방법론을 활용하여 미래 교통 시스템의 안전 관리 전략을 마련할 수 있으며, 도로 운영자와 관리자의 의사결정을 도울 수 있을 것으로 기대한다.

본 연구는 우리나라 대형산불의 진화에 있어 임도(산림도로)의 역할을 검증하고자 하였다. 연구대상지는 그간 발생한 대형산불 중 도로밀도가 가장 높은 지역 중 하나인 강원특별자치도 강릉시에서 발생한 2023년 4월 산불피해지역을 대상으로 하였다. 산불피해지역 범위는 현장확인하였으며, 산불의 피해강도는 Sentinel-2 영상을 통해 분석하였다. 이후, 피해범위 및 강도와 산림도로의 관계를 살펴보았다. 전체 149.1ha의 산불피해지역에 쉽게 접근할 수 있는, 피해지역 경계로부터 50m 이내에 조성된 도로는 약 59.6km로, 인접지역을 포함한 산불피해지역의 도로밀도는 무려 168.9m/ha에 달했다. 도로에 의해 단절된 산림은 모두 83개소로 파편화되어 있었는데, 이들 산림은 모두 비산화에 의한 확산으로 판단할 수 있어, 도로가 산불의 차단선 역할을 하지 못했음이 확인되었다. 진화차량 접근의 용이성에 따른 피해정도를 살펴보기 위해 도로로부터의 거리별 피해강도 분포를 살펴본 결과, 낮은 강도의 피해를 입은 지역은 오히려 도로에서 75m이상 떨어진 곳에서 비율이 대폭 높아짐이 확인되었다. 진화인력의 접근 용이성에 따른 피해정도를 살펴보기 위해 해발고별 피해강도 분포를 살펴본 결과 약한 강도의 피해를 입은지역 비율은 해발고가 높아질수록 늘어난 반면, 강한 강도 이상의 피해지역은 반대로 해발고가 높아질수록 비율이 줄어들었다. 강릉시 난곡동 산불피해지역에서 산림내부 혹은 인접한 도로가 산불진화에 효과적이라는 데이터는 없는 것으로 확인되었다. 이상의 결과는 산림 내 임도밀도를 높이는 것이 산불진화에 효과적이라는 논리와 배치된다. 강릉시 난곡산불지역의 경우 현재 산림청이 주장하는 우리나라 임도밀도인 3.9m/ha에 비해 무려 43배나 높다.

PURPOSES : This study aims to analyze the causes of pedestrian traffic accidents on community roads. METHODS : This study collected variables affecting pedestrian traffic accidents on community roads based on field surveys and analyzed them using negative binomial regression and zero-inflated negative binomial regression models. RESULTS : Model analysis results showed that the negative binomial regression model is more suitable than the zero-inflation negative binomial regression model. Additionally, the segment length (m), pedestrian volume (persons/15 min), traffic volume (numbers/15 min.), the extent of illegal parking, pedestrian-vehicle conflict ratio, and one-way traffic (one: residential, two: commercial) were found to influence pedestrian traffic accidents on community roads. Model fitness indicators, comparing actual values with predicted values, showed an MPB of 1.54, MAD of 2.57, and RMSE of 7.03. CONCLUSIONS : This study quantified the factors contributing to pedestrian traffic accidents on community roads by considering both static and dynamic elements. Instead of uniformly implementing measures, such as pedestrian priority zones and facility improvements on community roads, developing diverse strategies that consider various dynamic factors should be considered.

PURPOSES : This study aims to conduct a sensitivity analysis to determine the major factors affecting traffic accidents involving elderly pedestrians. METHODS : In this study, a regression tree model was built based on a non-parametric statistical model using data on traffic accidents involving elderly pedestrians. Using this model, we analyzed the degree of change in the probability of pedestrian fatalities. RESULTS : Results of the model analysis show that the first major factor combination affecting traffic accidents involving elderly pedestrians is speeding, night time, and road markers. The second combination is night time and arterial roads (national and local highways). The last combination that may lead to such accidents is heavy vehicles and federally funded local highways. CONCLUSIONS : Preventive measures, such as speed control, proper lighting, median strips, designation of pedestrian protection zones, and guidance of detours, are necessary to manage high-risk combinations causing accidents of the elderly.

PURPOSES : It is necessary to implement traffic-control strategies for underground roads. In this study, the application criteria for traffic control were developed to minimize actual traffic congestion on underground roads before it occurs. In particular, the traffic congestion judgement criteria and procedure (TJCAP) were developed. They can specifically classify the possibility of traffic congestion underground. METHODS : A microscopic traffic simulation model was used to analyze different scenarios. With the scenario simulation results, a hierarchical clustering analysis was applied to produce quantitative values from the TJCAP for each experimental network case. RESULTS : For network case (a), it was concluded that the possibility of traffic congestion on underground roads increases when the speed of the ground road connected to the main underground road and the connected ground road after the outflow of the ramp section is low. When the connected road is an interrupted facility after entering the underground roads, the red time is long, and when the section travel speed is 15 km/h, the possibility of traffic congestion underground is highest. A cluster analysis based on these results was performed using two techniques (elbow and silhouette) to verify the final classification. CONCLUSIONS : The TJCAP were designed to operate traffic flow with stricter criteria than traffic congestion management on ground roads. This reflects the difference in the driving environment between underground and above-ground roadways.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.